Hadoop access via hadoop interface services based on function conversion

ABSTRACT

The present description refers to a computer implemented method, computer program product, and computer system for receiving a resource request at an in-memory database platform that includes an application server and an in-memory database, generating a Hadoop connection function call based on the resource request, forwarding the Hadoop connection function call to a function call conversion service, identifying which of a plurality of Hadoop interface services correspond to the Hadoop connection function call, generating a Hadoop interface service function call corresponding to the Hadoop connection function call based one or more parameters included in the Hadoop connection function call, and forwarding the Hadoop interface service function call to the identified Hadoop interface service to initiate processing by a Hadoop cluster.

TECHNICAL FIELD

This description is directed generally to in-memory database systems andHadoop distributed processing systems, and in particular, to acomputer-implemented method, apparatus, and computer program product forHadoop access via Hadoop interface services based on a functionconversion.

BACKGROUND

An in-memory database system (IMDS) is typically a database managementsystem that stores all, or at least most, data in main memory whilestoring the data on disk or SSD for durability and recovery reasons.This contrasts to traditional on-disk database systems. Because workingwith data in-memory is much faster than reading data from and writingdata to disk systems, the IMDS can typically perform data managementfunctions much faster and with more predictable response times thantraditional database systems.

Hadoop is a distributed processing platform that allows data-intensiveoperations to be processed in a distributed fashion. A Hadoop clustercommonly includes a master node and a group of worker nodes. A jobrequest may be divided into a plurality of tasks, and the tasks may bedistributed to a group of worker nodes within the Hadoop platform to beprocessed in parallel.

SUMMARY

In one general aspect, a computer program product is provided. Thecomputer program product is tangibly embodied on a computer-readablestorage medium and includes executable code that, when executed, isconfigured to cause at least one data processing apparatus to receive aresource request at an in-memory database platform that includes anapplication server and an in-memory database, generate a Hadoopconnection function call based on the resource request, forward theHadoop connection function call to a function call conversion service,identifying which of a plurality of Hadoop interface services correspondto the Hadoop connection function call, generate a Hadoop interfaceservice function call corresponding to the Hadoop connection functioncall based one or more parameters included in the Hadoop connectionfunction call, and forward the Hadoop interface service function call tothe identified Hadoop interface service to initiate processing by aHadoop cluster.

In another general aspect, a computer implemented method is providedthat includes receiving a resource request at an in-memory databaseplatform that includes an application server and an in-memory database,generating a Hadoop connection function call based on the resourcerequest, forwarding the Hadoop connection function call to a functioncall conversion service, identifying which of a plurality of Hadoopinterface services correspond to the Hadoop connection function call,generating a Hadoop interface service function call corresponding to theHadoop connection function call based one or more parameters included inthe Hadoop connection function call, and forwarding the Hadoop interfaceservice function call to the identified Hadoop interface service toinitiate processing by a Hadoop cluster.

In another general aspect, an apparatus includes an in-memory databaseplatform including an in-memory database system and an applicationserver. The apparatus also includes a Hadoop cluster coupled to thein-memory database platform. The Hadoop cluster includes a plurality ofworker nodes and a master node that includes a plurality of Hadoopinterface services and a map-reduce engine. The application server isconfigured to receive a resource request, select one of the in-memorydatabase and the Hadoop cluster for at least partially processing theresource request, send a first function call to the in-memory databasesystem if the in-memory database system is selected for at leastpartially processing the resource request, and send a Hadoop connectionfunction call, via a function call conversion service, to one of theHadoop interface services for processing by the Hadoop cluster if theHadoop cluster is selected for at least partially processing theresource request.

The subject matter described in this specification can be implemented asa method or as a system or using computer program products, tangiblyembodied in information carriers, such as a CD-ROM, a DVD-ROM, asemiconductor memory, and a hard disk. Such computer program productsmay cause a data processing apparatus to conduct one or more operationsdescribed herein.

In addition, the subject matter described herein may also be implementedas a system including a processor and a memory coupled to the processor.The memory may encode one or more programs that cause the processor toperform one or more of the method acts described in this specification.

The details of one or more implementations are set forth in theaccompanying drawings and the description below. Other features will beapparent from the description and drawings, and from the claims.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram illustrating a system 100 according to anexample implementation.

FIG. 2 is a block diagram of a system 100 according to an exampleimplementation.

FIG. 3 is a flow chart illustrating operation of a system 100 accordingto an example implementation.

DETAILED DESCRIPTION

In the following, a detailed description of examples will be given withreference to the drawings. It should be understood that variousmodifications to the examples may be made. In particular, elements ofone example may be combined and used in other examples to form newexamples.

FIG. 1 is a block diagram illustrating a system 100 according to anexample implementation. System 100 includes an in-memory databaseplatform 110 coupled to a client application 124. A Hadoop cluster 130is coupled to the in-memory database platform 110 and providesdistributed processing across multiple database nodes. This architectureillustrated in FIG. 1 for system 100 may allow requests for eitherin-memory database system processing and/or Hadoop processing to behandled (e.g., received and/or processed) within one system.

According to an example implementation, in-memory database platform 110may include an in-memory database system 112 to provide in-memorydatabase services, such as rapid storage and retrieval of data from mainmemory. In-memory database platform 110 may also include an integratedapplication server 114 that may be integrated with the in-memorydatabase system 112. By application server 114 being integrated with thein-memory database system 112, this may include for example, a directinter-process communication between application server 114 and system112 (e.g., processes of server 114 and system 112 in directcommunication and), application server 114 and system 112 located onsame computer or server (on same physical machine), use of shared memorybetween the application server 114 and system 112 (e.g., where bothapplication server 114 and system 112 may read and write to a samememory), and/or the possibility to move processing steps from theapplication server to in-memory database system 112. These are merelysome examples of how application server 114 may be integrated within-memory database system 112, and other examples may be provided.

Application server 114 may generally provide application-relatedservices, such as, for example, providing web pages to one or moreclient applications, security/authentication, receiving and processingresource requests and other services. According to an exampleimplementation, application server 114 may include a client interface122 for providing an interface to client application 124. Clientapplication 124 may include, for example, a web browser or other clientapplication. Client interface 122 may provide services to clients/clientapplications, such as security and/or authentication services. Clientinterface 122 may also serve or send web pages to a client (such as toclient application 124), and may receive resource (or processing)requests, such as, Hyper-Text Transfer Protocol (HTTP) requests (e.g.,HTTP Get requests, or HTTP Post requests), or other requests. The termsresource request and processing request may be used interchangeablyherein. Client interface 122 may then forward the resource request toapplication logic 120, which may be logic or software running as ajavascript application running or executing within javascript (JS)container 116, according to one example implementation. Alternatively,resource requests may be received by application logic 120 from otherapplications running within the application server, e.g., as ajavascript application running within JS container 116, or from otherapplications within application server 114.

After receiving a processing request or a resource request from anapplication, application logic 120 of application server 114 may issueor send one or more resource requests or function calls that requestprocessing or service, to the in-memory database system 112 and/or theHadoop cluster 130, depending on what processing is required to fulfillor complete the received resource request. Therefore, application logic120 may first determine whether a request or function call should besent to either the in-memory database system 112 and/or the Hadoopcluster 130 to fulfill the received resource request. Such request orfunction call may then be generated by the application logic 120 andsent to either the in-memory database system 112 or the Hadoop cluster130 for processing. In some cases, one or more processing steps may beperformed by both the in-memory database system 112 and the Hadoopcluster 130 in order to fulfill or complete processing for the initiallyreceived resource request from the client. According to an exampleimplementation, after processing is completed, application server 114may return a result to the requesting application (e.g., clientapplication 124), via client interface 122, in response to the receivedresource request or processing request.

For example, if the resource request requires processing by thein-memory database system 112, the application logic 120 may issue orsend a resource request, e.g., via a function call or other request, toone or more of the core services, such as to core service 1, coreservice 2 (not shown), and/or core service N within application server114. The core service that receives the processing request or functioncall may then perform the requested processing or resource request on orvia the in-memory database system 112. For example, some of the coreservices may provide read and write operations to the in-memory databasesystem 112, and/or to perform other processing, to be performed on or bythe in-memory database system 112. For example, in response to aprocessing request, core service 1 may perform structured query language(SQL) processing or issue a SQL request, e.g., to perform a read from orwrite to in-memory database system 112.

For example, a HTTP Post request may be received by client interface 122based on a web page served to client application 124. The Post requestmay include, as parameters, a first name and a last name entered by auser via client application 124. Application logic 120 may receive thePost request, e.g., forwarded by client interface 122. Application logic120 may then, for example, store this information (the received firstname, last name) in the in-memory database system 112, e.g., by sendinga request or function call to one of the core services withinapplication server 114 to store such data, in the event that such datashould be stored in the in-memory database system 112. This data (firstname and last name) would then be stored in the in-memory databasesystem 112, e.g., via SQL operation performed by one of the coreservices.

Hadoop cluster 130 may include multiple nodes to provide distributedprocessing and/or parallel processing and distributed storage of data.In one example implementation, each node (e.g., master nodes and workernodes) may include a processor, memory, and other related hardware andsoftware. While only one Hadoop cluster 130 is shown, any number ofHadoop clusters may be provided to allow distributed or parallelprocessing.

In the example shown in FIG. 1, Hadoop cluster 130 may include aplurality or group of (e.g., N) worker nodes, such as worker node 1,worker node 2 (not shown), . . . worker node N, where each node mayprocess and store data, such that the Hadoop cluster 130 can performdistributed or parallel processing on data. Each worker node may includea task tracker to receive and process a task from the map-reduce engine138 of the master node 132. Each worker node may also include a Hadoopdistributed file system (HDFS) to store data, which allows data to bestored in a distributed manner across multiple worker nodes. As anexample, worker nodes 1 and N may include task trackers 140 and 144,respectively, and may include Hadoop distributed file systems (HDFSs)142 and 146, respectively.

In the example shown in FIG. 1, Hadoop cluster 130 also includes amaster node 132, which includes a map-reduce (M/R) engine 138.Map-reduce engine 138 may include a job tracker (not shown) forreceiving map-reduce jobs from applications, and dividing a job into anumber of tasks (or sub-jobs). The map-reduce engine 138 may thendistribute the tasks among or to each of the worker nodes, e.g., workernode 1, worker node 2 (not shown), . . . worker node N. Each worker nodemay then perform the requested task or sub-job, and may store the taskresults in its respective HDFS. The job tracker of the map-reduce engine138, in some cases, may then collect the processing results as one ormore result files from the worker nodes and may write or store theprocessing results to application server 114 or to in-memory databasesystem 112. According to one example implementation, the master node 132may provide or report the processing result back to the clientapplication that submitted the map-reduce job request.

Or, in another example implementation, a callback function may besubmitted to master node 132 as a map-reduce job to request thatprocessing results be stored in the in-memory database system 112.Master node 132 may then issue a new set of tasks to each of the workernodes where the processing results are stored to cause these workernodes to write or store the processing results directly to the in-memorydatabase system 112. Alternatively, the Hadoop worker nodes may providethe processing results to the application server 114, e.g., via functioncall conversion 135. Once application server 114 receives the processingresults from the Hadoop worker nodes, the application server may send arequest to the core services to store these processing results in thein-memory database system 112. Thus, the processing results from theHadoop worker nodes may be stored in the in-memory database system 112via application server 114.

According to an example implementation, one or more Hadoop interfaceservices 133 may be provided that may provide applications with accessto Hadoop services, while, at least in some cases, abstracting some ofthe details of Hadoop, for example. Two example Hadoop interfaceservices 133 include a Pig service 134 and a Hive service 136.

Pig service 134 is a service built on top of Hadoop which abstracts someof the details of Hadoop from an application. Pig service 134 receivesrequests written in a language known as “Pig Latin,” and may generateone or more map-reduce jobs based on received Pig commands or Pigrequests written in Pig Latin. In some cases, a collection or group ofPig requests or commands can be performed as one map-reduce job, forexample. Pig Latin abstracts the details of the map-reduce requests intoa higher level language, so that it may be easier for applications tosubmit requests for Hadoop processing via the Pig service 134.

Hive service 136 is a data warehouse service built on top of the Hadoopcluster 130 which facilitates querying and managing large datasetsresiding in a distributed file system. For example, with Hive service136, structured data in HDFS can be modeled as relational tables andHive service 136 can perform structured query language (SQL)-likeoperations on them with multiple chained map-reduce jobs. Hive service136 may receive requests, which may be known as Hive queries or a HiveQuery Language (Hive QL) requests, for example.

According to an example implementation, a Hadoop connection functionlibrary 118 is provided within or on application server 114 thatincludes a group or library of Hadoop connection functions. Hadoopconnection function library 118 provides a connection between one ormore applications (e.g., applications which may be running within JScontainer 116, or application logic 120, or other applications) and theHadoop interface services 133 (e.g., Pig service 134, Hive service 136)without requiring the application to know all of the details of theunderlying Hadoop interface services 133. Thus, some of the specificdetails of Hive service 136 or Pig service 134 (and other Hadoopinterface services 133) may be abstracted or hidden from an applicationby calling one or more functions of the Hadoop connection functionlibrary 118, instead of directly submitting Hive requests or Pigrequests to Hive service 136 or Pig service 134, respectively.

The Hadoop connection function library 118 is a collection or library ofconnection functions provided in javascript or other language. Eachconnection function in the connection function library 118 may map to acorresponding Hive request or Pig request. Therefore, to allow anapplication to access Hive/Pig services (or to access Hadoop servicesvia Pig service/Hive service), an application or application logic 120may generate a Hadoop connection function call, which is a call to oneof the connection functions in the Hadoop connection function library118. This may allow an application within application server 114, orapplication logic 120, to obtain access to services of Hadoop cluster130 via Pig service 134 or Hive service 136 by issuing a call to aconnection function written in javascript (or other language) withoutthe requesting application or application logic 120 needing to know allof the details of Pig service 134 or Hive service 136, and e.g., withoutrequiring to the requesting application/application logic 120 to handleany errors from such Hive/Pig services. The connection function library118 also allows an application, or application logic 120, to issue orsend processing requests (or resource requests) to either the in-memorydatabase system 112 (e.g., by sending a processing request or functioncall to one of the core services) and/or send a request for processingby the Hadoop cluster 130 by generating and sending a Hadoop connectionfunction call to function call conversion service 135.

In response to receiving a Hadoop connection function call (e.g., fromapplication logic 120 or an application running within JS container 116or elsewhere within application server 114), the Hadoop connectionfunction library 118 may forward the Hadoop connection function call tofunction call conversion service 135. According to an exampleimplementation, function call conversion service 135 may be a program orlogic that may, for example, reside or run/execute on master node 132,or other node or location. According to an example implementation,function call conversion service 135 may convert the received Hadoopconnection function call to a corresponding Hadoop interface servicefunction call, e.g., based on one or more parameters (such as functionname and other parameters) of the received Hadoop connection functioncall. A Hadoop connection function call may include the name of theHadoop connection function, and one or more additional parameters. Forexample, function call conversion service 135 may convert the receivedHadoop function call to a corresponding Pig request or Hive request. Inother words, by performing such function call conversion, function callconversion service 134 may generate a Hadoop interface service functioncall (e.g., Pig request or Hive request) based on a received Hadoopconnection function call.

For example, a Hadoop connection function call may be written as:Hadoop_connection_function_name (parameter1, parameter2, parameter3),wherein Hadoop_connection_function_name is a name of the Hadoopconnection function, and parameter1, parameter2 and parameter3 areadditional parameters of this function call. The function callconversion service 135 may generate a corresponding Pig or Hive functioncall, such as: Pig_function_name (parameter2, parameter3, parameter4),where two of the three parameters of the Hadoop function call(parameter2 and parameter3) are also parameters of the corresponding Pigfunction call, but parameter4 is a new parameter (parameter4 is notpresent or not provided via the Hadoop connection function call).Function call conversion 135 may also handle any errors that may begenerated by the Pig service 134 or Hive service 136.

Function call conversion service 135 then forwards the generated Hadoopinterface service function call (e.g., Pig function call, or Hivefunction call) to the identified or corresponding Hadoop interfaceservice. For example, a generated Pig function call or Pig request wouldbe forwarded to the Pig service 134, while a generated Hive functioncall or Hive request would be forwarded to the Hive service 136.

The receiving Hadoop interface service (e.g., Pig service 134 or Hiveservice 136) may then generate a map-reduce job (or one or moremap-reduce jobs) based on the received Hadoop interface service functioncall. For example, Hive service 136 may generate a map-reduce job foreach received Hive function call or Hive request received from functioncall conversion service 135. Similarly, Pig service 134 may generate amap-reduce job for each received Pig function call or each Pig requestfrom function call conversion service 135. Alternatively, Pig service134 may receive multiple Pig function calls or multiple Pig requests,and may generate just one or two map-reduce jobs based on a group of,e.g., 4, 5, 6, . . . Pig function calls or Pig requests, since Pigservice 134 may have the capability of combining multiple Pig requestsinto a single map-reduce job, according to an example implementation.

As noted, a map-reduce engine 138 of master node 132 may receive themap-reduce job from the Pig service 134 or Hive service 136. Themap-reduce engine 138 may then divide the map-reduce job into aplurality of tasks, and then distribute the tasks to each of a pluralityof worker nodes. The result file (or result files that are generated byworker nodes processing the tasks) may then be returned to the functioncall conversion service 135, the application logic 120, the in-memorydatabase system 112, and/or the application server 114, as examples.According to an example implementation, a callback function call may besent by the function call conversion service 135 to one of the Hadoopinterface services 133 requesting that the processing result file bestored directly into the in-memory database system 112, e.g., where thefunction call may identify an address, a table, or a resource locationwhere the result file should be stored in the in-memory database system112. In this example, after processing the tasks for the map-reduce job,the worker nodes may write or store their result files in the in-memorydatabase system 112, e.g., in the location or file identified by thecallback function.

An example will now be described that may involve accesses to both thein-memory database system 112 and the services of the Hadoop cluster 130via the Hadoop interface services 132. According to an example, an HTTPrequest may be received by client interface 122 from client application124 (e.g., web browser) that includes a user's first name and last name.The HTTP request is forwarded to the application logic 120. Applicationlogic 120 then issues a read request to the core services to perform atable lookup in the in-memory database system 112 to obtain a user_ID(or user identification number) associated with the user's name (firstname, last name) received in the HTTP request. The core services submita SQL request to the in-memory database system 112 to obtain the user_IDcorresponding to the user's first name and last name. The user_ID isthen returned by the in-memory database system 112 to the applicationlogic 120 via the core services.

Next, according to an illustrative example, one or more requests forHadoop processing may be sent by application logic 120 to one or more ofthe Hadoop interface services 133 in order for the application logic 120to obtain from the Hadoop cluster 130 a list of product IDs for top 10recommended products for the user associated with the received user_ID.For example, application logic 120 may generate and send one or more, oreven a group (or plurality) of Hadoop function calls, to the Hadoopconnection function library 118. The Hadoop connection function library118 may then forward each of the Hadoop connection function calls to thefunction call conversion service 135.

For example, application logic 120 may generate and submit, via theHadoop connection function library 118, a group (or plurality) of Hadoopconnection function calls to the function call conversion service 135including the four example Hadoop connection function calls listed belowin Table 1. According to an example implementation, function callconversion service 135 may then generate a corresponding Pig Latinfunction call (or Pig request) for each of the four Hadoop connectionfunction calls, which are sent to Pig service 134, so that Hadoopcluster 130 can perform one or more requested functions.

TABLE 1 Example Function Conversion from Hadoop Connection Function toCorresponding Pig Latin Function Example Hadoop Connection Function call(forwarded Example Pig Latin Function call by Hadoop connection(generated by function call function library 118) conversion service135) 1. createpiginnerjoin (cjoin, cjoin = join user_IDs_table byuser_IDs_table (UID), UID, user's_product_review_tableuser's_product_review_table by user_ID (userID) 2. createpiglimitcat_top = limit cjoin by 10 (user_IDs_table, cat_top, 10) 3.createpigstoreDB (cat_top, store cat_top into dest_table_locationdest_table_location) [URL to destination table in in- memory databasesystem 112 to store cat_top] 4. executepig (displayresult) Run

As part of this example, the Hadoop cluster 130 may periodically and/orcontinuously determine and maintain one or more tables that may,together, be used to determine a list of top 10 recommended products forthe user_ID, as an illustrative example. For example, Hadoop cluster 130may maintain and periodically update a user_IDs_table that identifies agroup of similar users for each user_ID based upon users that havesubmitted similar product reviews as the user of the user_ID (e.g.,submitted product reviews having similar star ratings for the sameproduct). Hadoop cluster 130 may also determine and periodically updatea table that identifies a user's_product_review_table that identifiesproducts purchased and rated/reviewed by each user_ID, where, forexample, this table may include highest rated products (which have beenrated by the user/user_ID) at the top of the table for each user_ID, andlower rated products at the bottom of the table. These are merelyexamples of the type of data that may be stored and periodically updatedwithin the Hadoop cluster 130, and many other types of information maybe stored or processed within a Hadoop cluster. The four example Hadoopfunction calls listed in Table 1 will be briefly described. The firstHadoop connection function call, createpiginnerjoin (cjoin,user_IDs_table (UID), user's_product_review_table (userID), is a requestto join the two tables (user_IDs_table, user's_product_review_table)based on the common field, user_ID. Based on the mapping shown in Table1 for this Hadoop connection function, function call conversion service135 generates the corresponding Pig Latin function call as cjoin=joinuser_IDs_table by UID, user's_product_review_table by user_ID, whichcauses the Hadoop cluster 130 to join these two tables based on user_ID.The result of this table join operation is a table, which may betemporarily referred to as cjoin, which includes product IDs forproducts that are recommended to each user_ID, based on similar users orsimilar product reviews.

The cjoin table, listing recommended products for each user_ID, may havemany, e.g., hundreds, of recommended products for each user_ID.Therefore, application logic 120 sends, via Hadoop connection functionlibrary 118, the second Hadoop connection function, createpiglimit(user_IDs_table, cat_top, 10), which is a function call to limit theoutput of the previous function to the top 10 values. Function callconversion service 135 then generates the corresponding Pig Latinfunction call (or Pig request) as cat_top=limit cjoin by 10, whichrequests the Pig service 134 to limit the cjoin table to the top 10product IDs. Therefore, the resulting table, cat_top, will include theproduct IDs of the top ten recommended products for each user_ID. Thus,for this second function call, the reference name (cat_top) is passed asa parameter of the second Hadoop connection function call and is used asthe reference name of the corresponding Pig Latin function call. Also,the number 10 (indicating that only the top 10 product IDs should beprovided for each user_ID in the result file) is also included in thecorresponding Pig Latin function call (limit cjoin by 10).

Next, application logic 120 sends to function call conversion service135, via Hadoop connection function library 118, the third Hadoopconnection function, createpigstoreDB (cat_top, dest_table_location),which is a request to store the cat_top table (listing top 10 productIDs for each user_ID) in a location (dest_table_location) withinin-memory database system 112. This third Hadoop connection functioncall is converted by function call conversion service 135 to acorresponding Pig Latin function call or Pig request as store cat_topinto dest_table_location. This may be considered a callback functionwhich may cause the worker nodes which have stored the resulting cat_toptable (listing top 10 product IDs for each user_ID) in their HDFS tostore their cat_top result tables directly in the in-memory databasesystem 112 at the location identified by the URL (uniform resourcelocator) specified by dest_table_location. Or, alternatively, the thirdHadoop connection function, createpigstoreDB (cat_top,dest_table_location), may cause worker nodes to return their cat_topresult tables to application server 114, and then application server 114may issue a write request (to core services (e.g., where the writerequest identifies the write location as dest_table_location), whichcauses the received cat_top result tables to be written to in-memorydatabase system 112 at the specified memory/storage location(dest_table_location). Thus, the parameter “dest_table_location”included in the third Hadoop connection function call is passed as aparameter of the corresponding Pig Latin function call or Pig request.Thus, in some cases, one or more parameters included in a Hadoopconnection function call are included in the corresponding Pig or Hiverequest. In this manner, for some function names or reference names fora function, and for some function parameters, the Hadoop connectionfunction calls may provide or encapsulate these parameters which maythen be used by the function call conversion service 135 to generate thecorresponding Pig/Hive request or included within the correspondingPig/Hive request, for example.

Application logic may then generate and send a fourth Hadoop connectionfunction as executepig (displayresult), which is a request to have Pigservice 134 execute or run the group of three previously submittedrequests, and then display/store the results as requested (e.g., directwrite result file back to in-memory database system 112). Function callconversion service 135 may then generate the corresponding Pig Latinfunction call or Pig request as run, which causes the Pig service 134 tocombine the four Pig requests, and generate one or maybe two map-reducejobs to perform the operations requested by these 4 function calls. Themap-reduce engine 138 may then divide this job into a plurality oftasks, and the tasks are distributed to the worker nodes. Per thecallback function of the third function call, the resulting files arestored by the worker nodes, e.g., via application server 114 and coreservices, in the designated location within the in-memory databasesystem 112, in this example.

Function call conversion service 135 is notified by Pig service that thePig execute/run request has been completed, and function call conversionservice 135 then notifies the application logic 120 that these Pigrequest(s) have been processed. Application logic 120 then sends arequest to core services to identify the top 10 product IDs for thereceived user_ID (corresponding to the received first name, last namefrom the client application). Alternatively, the result file stored inthe in-memory database system 112 simply identifies the product IDs forthe top 10 products corresponding to the user_ID. In this example,application logic 120 may then issue a request via core services to thein-memory database system 112 to provide product information (e.g.,product description, picture, price information, . . . ) for each of the10 product IDs. This information is returned by the in-memory databasesystem 112 to the application logic 120 via the core services.

Application logic then provides this product information for these top10 products to the client interface 122. Client interface 122 may thengenerate and serve a web page to client application 124 that displays orprovides this product information for the top 10 recommended productsfor this user (e.g., corresponding to the user_ID of this user). Thisinformation is then displayed to the user.

FIG. 2 is a block diagram of a system 100 according to an exampleimplementation. Receiving logic 220 (e.g., client interface 122) isconfigured to receive a resource request at an in-memory databaseplatform that includes an application server and an in-memory database.Hadoop connection function call generator 220 (e.g., application logic120) is configured to generate a Hadoop connection function call basedon the resource request. A Hadoop connection function call forwardinglogic 230 (e.g., Hadoop connection function library 118) is configuredto forward the Hadoop connection function call to a function callconversion service (e.g., 135). An interface service identificationlogic (e.g., function call conversion service 135) is configured toidentify which of a plurality of Hadoop interface services (e.g., Pigservice 135, Hive service 136) correspond to the Hadoop connectionfunction call. A Hadoop interface service function call generator 250(e.g., function call conversion service 135) is configured to generate aHadoop interface service function call corresponding to the Hadoopconnection function call based one or more parameters included in theHadoop connection function call. A Hadoop interface service functioncall forwarding logic 260 (e.g., function call conversion service 135)is configured to forward the Hadoop interface service function call tothe identified Hadoop interface service to initiate processing by aHadoop cluster.

FIG. 3 is a flow chart illustrating operation of a system 100 accordingto an example implementation. At 310, a resource request is received atan in-memory database platform (e.g., 110) that includes an applicationserver (e.g., 114) and an in-memory database (e.g., 112). At 320, aHadoop connection function call is generated (e.g., by application logic120 or an application) based on the resource request. At 330, the Hadoopconnection function call is forwarded (e.g., by Hadoop connectionfunction library 118) to a function call conversion service (e.g., 135).

At 340, it is identified (e.g., by function call conversion service 135)which of a plurality of Hadoop interface services correspond to theHadoop connection function call. For example, identifying the Hadoopinterface service that corresponds to the received Hadoop connectionfunction call may be performed based on a function name of the receivedHadoop connection function that is called, since each Hadoop connectionfunction name may map to either a Pig function call (Pig request), or aHive function call (Hive request), for example. Therefore, the functionname of the Hadoop connection function call may map to, or may be usedto identify, either the Pig service 134 or the Hive service 136 (orother Hadoop interface service).

At 350, a Hadoop interface service function call is generated (e.g., byfunction call conversion service 135) corresponding to the Hadoopconnection function call based one or more parameters included in theHadoop connection function call. At 360, the Hadoop interface servicefunction call is forwarded (e.g., by function call conversion service135) to the identified Hadoop interface service to initiate processingby a Hadoop cluster.

The method illustrated in FIG. 3 may further include generating, by theidentified Hadoop interface service, a map-reduce job based on theHadoop interface service function call, receiving, by a map-reduceengine of a master node of the Hadoop cluster, the map-reduce job fromthe identified Hadoop interface service, and, receiving, by thein-memory database from the Hadoop cluster (e.g., via function callconversion service 135 and application server 114), one or more resultfiles from the Hadoop cluster in response to forwarding the Hadoopinterface service function call.

The method illustrated in FIG. 3 may further include generating, by theidentified Hadoop interface service, a map-reduce job based on theHadoop interface service function call, receiving, by a map-reduceengine of a master node of the Hadoop cluster, the map-reduce job fromthe identified Hadoop interface service, dividing, by the map-reduceengine, the map-reduce job into a plurality tasks, and distributing, bythe map-reduce engine, the tasks to a plurality of worker nodes in theHadoop cluster.

In the method illustrated in FIG. 3 the identifying (operation 340) mayinclude identifying, by a function call conversion service, which of aPig service or a Hive service corresponds to the Hadoop connectionfunction call.

In the method illustrated in FIG. 3, the generating (operation 350) mayinclude generating, by a function call conversion service based on theHadoop connection function call, a Hive QL request, and wherein theforwarding (operation 360) may include forwarding the Hive QL request toa Hive service. The method may further include generating, by the Hiveservice, a map-reduce job based on the Hive QL request, and forwarding,by the Hive service, the map-reduce job to a map-reduce engine of amaster node of the Hadoop cluster.

In the method illustrated in FIG. 3, the generating (operation 350) mayinclude generating, by a function call conversion service based on theHadoop connection function call, a Pig request provided in Pig Latin,and wherein the forwarding the Hadoop interface service function callcomprises forwarding the Pig request to a Pig service. The method mayfurther include generating, by the Pig service, a map-reduce job basedon the Pig request, and forwarding, by the Pig service, the map-reducejob to a map-reduce engine of a master node of the Hadoop cluster.

Implementations of the various techniques described herein may beimplemented in digital electronic circuitry, or in computer hardware,firmware, software, or in combinations of them. Implementations mayimplemented as a computer program product, i.e., a computer programtangibly embodied in an information carrier, e.g., in a machine-readablestorage device or in a propagated signal, for execution by, or tocontrol the operation of, data processing apparatus, e.g., aprogrammable processor, a computer, or multiple computers. A computerprogram, such as the computer program(s) described above, can be writtenin any form of programming language, including compiled or interpretedlanguages, and can be deployed in any form, including as a stand-aloneprogram or as a module, component, subroutine, or other unit suitablefor use in a computing environment. A computer program that mightimplement the techniques mentioned above might be deployed to beexecuted on one computer or on multiple computers at one site ordistributed across multiple sites and interconnected by a communicationnetwork.

Method steps may be performed by one or more programmable processorsexecuting a computer program to perform functions by operating on inputdata and generating output. Method steps also may be performed by, andan apparatus may be implemented as, special purpose logic circuitry,e.g., an FPGA (field programmable gate array) or an ASIC(application-specific integrated circuit).

Processors suitable for the execution of a computer program include, byway of example, both general and special purpose microprocessors, andany one or more processors of any kind of digital computer. Generally, aprocessor will receive instructions and data from a read-only memory ora random access memory or both. Elements of a computer may include atleast one processor for executing instructions and one or more memorydevices for storing instructions and data. Generally, a computer alsomay include, or be operatively coupled to receive data from or transferdata to, or both, one or more mass storage devices for storing data,e.g., magnetic, magneto-optical disks, or optical disks. Informationcarriers suitable for embodying computer program instructions and datainclude all forms of non-volatile memory, including by way of examplesemiconductor memory devices, e.g., EPROM, EEPROM, and flash memorydevices; magnetic disks, e.g., internal hard disks or removable disks;magneto-optical disks; and CD-ROM and DVD-ROM disks. The processor andthe memory may be supplemented by, or incorporated in special purposelogic circuitry.

To provide for interaction with a user, implementations may beimplemented on a computer having a display device, e.g., a cathode raytube (CRT) or liquid crystal display (LCD) monitor, for displayinginformation to the user and a keyboard and a pointing device, e.g., amouse or a trackball, by which the user can provide input to thecomputer. Other kinds of devices can be used to provide for interactionwith a user as well; for example, feedback provided to the user can beany form of sensory feedback, e.g., visual feedback, auditory feedback,or tactile feedback; and input from the user can be received in anyform, including acoustic, speech, or tactile input.

Implementations may be implemented in a computing system that includes aback-end component, e.g., as a data server, or that includes amiddleware component, e.g., an application server, or that includes afront-end component, e.g., a client computer having a graphical userinterface or a Web browser through which a user can interact with animplementation, or any combination of such back-end, middleware, orfront-end components. Components may be interconnected by any form ormedium of digital data communication, e.g., a communication network.Examples of communication networks include a local area network (LAN)and a wide area network (WAN), e.g., the Internet.

While certain features of the described implementations have beenillustrated as described herein, many modifications, substitutions,changes and equivalents will now occur to those skilled in the art. Itis, therefore, to be understood that the appended claims are intended tocover all such modifications and changes as fall within the scope of theembodiments.

What is claimed is:
 1. A computer implemented method comprising:receiving a resource request at an in-memory database platform thatincludes an application server and an in-memory database; generating aHadoop connection function call based on the resource request;forwarding the Hadoop connection function call to a function callconversion service; identifying which of a plurality of Hadoop interfaceservices correspond to the Hadoop connection function call; generating aHadoop interface service function call corresponding to the Hadoopconnection function call based one or more parameters included in theHadoop connection function call; and forwarding the Hadoop interfaceservice function call to the identified Hadoop interface service toinitiate processing by a Hadoop cluster.
 2. The method of claim 1 andfurther comprising: generating, by the identified Hadoop interfaceservice, a map-reduce job based on the Hadoop interface service functioncall; receiving, by a map-reduce engine of a master node of the Hadoopcluster, the map-reduce job from the identified Hadoop interfaceservice; receiving, by the in-memory database from the Hadoop cluster,one or more result files from the Hadoop cluster in response toforwarding the Hadoop interface service function call.
 3. The method ofclaim 1 and further comprising: generating, by the identified Hadoopinterface service, a map-reduce job based on the Hadoop interfaceservice function call; receiving, by a map-reduce engine of a masternode of the Hadoop cluster, the map-reduce job from the identifiedHadoop interface service; dividing, by the map-reduce engine, themap-reduce job into a plurality tasks; and distributing, by themap-reduce engine, the tasks to a plurality of worker nodes in theHadoop cluster.
 4. The computer implemented method of claim 1 whereinthe identifying which of a plurality of Hadoop interface servicescorrespond to the Hadoop connection function call comprises identifying,by a function call conversion service, which of a Pig service or a Hiveservice corresponds to the Hadoop connection function call.
 5. Thecomputer implemented method of claim 1: wherein the generating a Hadoopinterface service function call comprises generating, by a function callconversion service based on the Hadoop connection function call, a HiveQL request; and wherein the forwarding the Hadoop interface servicefunction call comprises forwarding the Hive QL request to a Hiveservice.
 6. The computer implemented method of claim 5 and furthercomprising: generating, by the Hive service, a map-reduce job based onthe Hive QL request; and forwarding, by the Hive service, the map-reducejob to a map-reduce engine of a master node of the Hadoop cluster. 7.The computer implemented method of claim 1: wherein the generating aHadoop interface service function call comprises generating, by afunction call conversion service based on the Hadoop connection functioncall, a Pig request provided in Pig Latin; and wherein the forwardingthe Hadoop interface service function call comprises forwarding the Pigrequest to a Pig service.
 8. The computer implemented method of claim 7and further comprising: generating, by the Pig service, a map-reduce jobbased on the Pig request; and forwarding, by the Pig service, themap-reduce job to a map-reduce engine of a master node of the Hadoopcluster.
 9. A computer program product, the computer program productbeing tangibly embodied on a computer-readable storage medium andincluding executable code that, when executed, is configured to cause atleast one data processing apparatus to: receive a resource request at anin-memory database platform that includes an application server and anin-memory database; generate a Hadoop connection function call based onthe resource request; forward the Hadoop connection function call to afunction call conversion service; identify which of a plurality ofHadoop interface services corresponds to the Hadoop connection functioncall; generate a Hadoop interface service function call corresponding toHadoop connection function call based one or more parameters included inthe Hadoop connection function call; and forward the Hadoop interfaceservice function call to the identified Hadoop interface service toinitiate processing by a Hadoop cluster.
 10. The computer programproduct of claim 9 and including code that, when executed, is configuredto further cause the at least one data processing apparatus to receive,by the in-memory database from the Hadoop cluster via the applicationserver, one or more result files from the Hadoop cluster in response toforwarding the Hadoop interface service function call.
 11. The computerprogram product of claim 9 and including code that, when executed, isconfigured to further cause the at least one data processing apparatusto: generate, by the identified Hadoop interface service, a map-reducejob based on the Hadoop interface service function call; receive, by amap-reduce engine of a master node of the Hadoop cluster, the map-reducejob from the identified Hadoop interface service; divide, by themap-reduce engine, the map-reduce job into a plurality of tasks; anddistribute, by the map-reduce engine, the tasks to a plurality of workernodes in the Hadoop cluster.
 12. The computer program product of claim 9and wherein the code being configured to cause the at least one dataprocessing apparatus to identify which of a plurality of Hadoopinterface services correspond to the Hadoop connection function callcomprises code configured to cause the data processing apparatus toidentify, by a function call conversion service, which of a Pig serviceor a Hive service corresponds to the Hadoop connection function call.13. The computer program product of claim 9: wherein the code beingconfigured to cause the at least one data processing apparatus togenerate a Hadoop interface service function call comprises codeconfigured to cause the data processing apparatus to generate, by afunction call conversion service based on the Hadoop connection functioncall, a Hive Query Language (QL) request; wherein the code beingconfigured to cause the at least one data processing apparatus toforward the Hadoop interface service function call comprises code beingconfigured to cause the data processing apparatus to forward the Hive QLrequest to a Hive service.
 14. The computer program product of claim 13and including code that, when executed, is configured to further causethe at least one data processing apparatus to: generate, by the Hiveservice, a map-reduce job based on the Hive QL request; and forward, bythe Hive service, the map-reduce job to a map-reduce engine of a masternode of the Hadoop cluster.
 15. The computer program product of claim 9:wherein the code configured to cause the data processing apparatus togenerate a Hadoop interface service function call comprises codeconfigured to cause the data processing apparatus to generate, by afunction call conversion service based on the Hadoop connection functioncall, a Pig request provided in Pig Latin; wherein the code configuredto cause the data processing apparatus to forward the Hadoop interfaceservice function call comprises code configured to cause the dataprocessing apparatus to forward the Pig request to a Pig service. 16.The computer program product of claim 15 and including code that, whenexecuted, is configured to further cause the at least one dataprocessing apparatus to: generate, by the Pig service, a map-reduce jobbased on the Pig request; and forward, by the Pig service, themap-reduce job to a map-reduce engine of a master node of the Hadoopcluster.
 17. An apparatus comprising: an in-memory database platformincluding an in-memory database system and an application server; theapplication server configured to: receive a resource request; generate aHadoop connection function call based on the resource request; forwardthe Hadoop connection function call to a function call conversionservice; and a function call conversion service configured to: identifywhich of a plurality of Hadoop interface services corresponds to theHadoop connection function call; convert the Hadoop connection functioncall to a corresponding Hadoop interface service function call based oneor more parameters included in the Hadoop connection function call; andforward the Hadoop interface service function call to the identifiedHadoop interface service to initiate processing by a Hadoop cluster. 18.The apparatus of claim 17 and further comprising the Hadoop cluster thatincludes a plurality of worker nodes and a master node, the master nodeincluding a plurality of Hadoop interface services and a map-reduceengine; the identified Hadoop interface service configured to generate amap-reduce job based on the Hadoop interface service function call; themap-reduce engine configured to: receive the map-reduce job from theidentified Hadoop interface service; divide the map-reduce job into aplurality of tasks; and distribute the tasks to a plurality of workernodes in the Hadoop cluster.
 19. The apparatus of claim 17 wherein theplurality of Hadoop interface services comprise a Hive service and a Pigservice.
 20. The apparatus of claim 18 wherein the apparatus isconfigured to receive, by the in-memory database from the Hadoopcluster, one or more result files in response to distributing the tasksto the plurality of worker nodes in the Hadoop cluster.
 21. Theapparatus of claim 17 wherein the function call conversion service beingconfigured to convert the Hadoop connection function call to acorresponding Hadoop interface service function call comprises thefunction call conversion service configured to convert the Hadoopconnection function call to a corresponding Hive Query Language (QL)request; wherein the function call conversion service being configuredto forward the Hadoop interface service function call comprises thefunction call conversion service being configured to forward the Hive QLrequest to a Hive service.
 22. The apparatus of claim 17 wherein thefunction call conversion service being configured to convert the Hadoopconnection function call to a corresponding Hadoop interface servicefunction call comprises the function call conversion service configuredto convert the Hadoop connection function call to a corresponding Pigrequest; wherein the function call conversion service being configuredto forward the Hadoop interface service function call comprises thefunction call conversion service being configured to forward the Pigrequest to a Pig service.
 23. An apparatus comprising: an in-memorydatabase platform including an in-memory database system and anapplication server; a Hadoop cluster coupled to the in-memory databaseplatform, the Hadoop cluster including a plurality of worker nodes and amaster node that includes a plurality of Hadoop interface services and amap-reduce engine; the application server configured to: receive aresource request; select one of the in-memory database and the Hadoopcluster for at least partially processing the resource request; send afirst function call to the in-memory database system if the in-memorydatabase system is selected for at least partially processing theresource request; and send a Hadoop connection function call, via afunction call conversion service, to one of the Hadoop interfaceservices for processing by the Hadoop cluster if the Hadoop cluster isselected for at least partially processing the resource request.
 24. Theapparatus of claim 23 wherein the application server configured to senda Hadoop connection function call comprises: the application serverconfigured to perform the following if the Hadoop cluster is selectedfor processing the resource request: generate the Hadoop connectionfunction call based on the resource request; forward the Hadoopconnection function call to the function call conversion service; andthe function call conversion service configured to: identify which oneof the Hadoop interface services corresponds to the Hadoop connectionfunction call; convert the Hadoop connection function call to acorresponding Hadoop interface service function call based one or moreparameters included in the Hadoop connection function call; and forwardthe Hadoop interface service function call to the identified Hadoopinterface service within a Hadoop cluster.